Such methods are particularly useful when a method of location of the device by GPS (“Global Positioning System”) is not possible. This is for example often the case inside buildings.
Known methods of location use the measurements of an inertial platform housed inside the device itself to measure its direction of displacement and the amplitude of its displacement in this direction from a previous position. Among these known methods, some use particle filters to estimate the position of the device in the three-dimensional environment. Accordingly, particle filters exploit the fact that there exist predefined constraints on the displacements of the device inside the three-dimensional environment. For example, a typical constraint is that a displacement cannot pass through a wall.
Thus, these known methods of location typically comprise:
a) the provision of a map of the three-dimensional space and of predefined constraints on the displacements of the device in this three-dimensional space,
b) the generation, by an electronic computer, of several distinct particles, each particle being associated:
                with coordinates coding its position on the map, and        with a weight representing the probability that the device is situated at the site of this particle,c) the reception of measurements representative of the direction of displacement of the device and of the amplitude of this displacement from its previous position, these measurements being carried out by sensors onboard the displaced device,d) the updating of the coordinates of the position of each particle as a function of the measurements received during step c) and of a predetermined displacement law for displacing this particle from its previous position Pik-1 to a new position Pik in a manner correlated with the measured displacement of the device, each displacement law comprising for this purpose at least one first measured variable whose value is dependent on the measurement of the direction of displacement received during stepc) and a second measured variable whose value is dependent on the measurement of the amplitude of this displacement received during step c), and thene) for each particle, if the latest displacement of this particle from the position Pik-i to the position Pik satisfies the predefined constraints, the increasing of the weight associated with this particle with respect to the weights of the particles whose latest displacement infringes these predefined constraints,        the repetition of steps c) to e), andf) the estimation of the position of the device on the basis of the positions of the particles and of the weights associated with these particles by allotting, during this estimation, more importance to the positions of the particles associated with the highest weights.        
Such known methods of location of a device implementing a particle filter are for example disclosed in:                patent applications WO 2012158441 and U.S. Pat. No. 8,548,738 B1,        in the article O. Woodman et al., “Pedestrian localization for indoor environments”, ACM, 2008.        
Such a method is also disclosed in detail in the thesis of J. Straub,
“Pedestrian indoor localization and tracking using a particle filter combined with a learning accessibility map”, thesis, August 2010, Technical University of Munich. This thesis is downloadable at the following address:
http://people.csail.mit.edu/jstraub/download/Straub10PedestrianLocalization.pdf.
Subsequently, this thesis is referenced through the term “Straub 2010”.
Prior art is also known from:                EP2519803A, and        Krach B. et al., “Cascaded estimation architecture for integration of foot-mounted inertial sensors”, Position, location and navigation symposium, 2008, IEEE/ION, Piscataway, 2008 May 5.        
Under ideal conditions, these known methods make it possible to precisely estimate the position of the device. However, in reality, there may exist a constant bias in the direction of displacement of the device and/or in the amplitude of displacement of this device in this direction. This bias may have very different origins. For example, it may be caused by a bias in the measurements of one or more of the sensors of the inertial platform incorporated in the device. It may also be caused by an error in modeling the relation which links the measurements of the inertial platform to the directions of displacement and to the amplitude of this displacement. Finally, it may also be caused by an incorrect positioning of the device with respect to its direction of displacement.
The presence of a systematic bias such as this greatly degrades the precision of the estimation of the position of the device if it is not corrected. To correct such a bias, it is necessary to know its value. Now, in most cases, this value of the bias is not known in advance. Ideally, it would therefore be necessary to undertake a prior calibration phase to determine the value of the bias, and then use this value of the bias to correct the estimation of the position. However, obliging the user to execute a prior calibration phase before launching the location of the device is in most cases undesirable, or indeed quite simply impossible in certain cases such as for example when the bias evolves over time.